从多级 DataFrame 中提取索引作为列表中的字符串

Question

I have a Multi-level DataFrame with 36 entries: It's a pandas DF that has 36 levels (36 stocks) . Attached to a single-date time. For anyone curious, this is output that is created by using Zipline API, or Quantopians Pipeline API. So I don't have much control on how the dataframe is created.

As you see in the DF, each level is represented by an equity, or stock, for example: Equity(1251 [CAJ])

I'm trying to figure out how to extract the ticker symbol for each level in a string format and add them to a list! Such as list = ['CAJ', 'CBT', 'GILD', ...] followed by all other equities the program spit out.

This has caused me a huge headache and I've tried proceeding with:
result = df asset_list = result.index.levels[1] stocks = asset_list.get_level_values(0).unique()

But this does not seem to work and I get something way off, it might be an API issue as len(stocks) provides a value of 8830. I don't even know how that is possible.

Would greatly appreciate some coding help here. Please let me know if you need more information from my side.

As mentioned from the thread below thanks to @sammywemmy, here is a dictionary of the first 5 rows of the table above.

result.head().to_dict()

reproduces:

{'current ratio': {(Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(1251 [CAJ])): 1.921883,
  (Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(1315 [CBT])): 2.0836239999999999,
  (Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(3212 [GILD])): 3.1044160000000001,
  (Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(3460 [HAS])): 5.3676060000000003,
  (Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(3798 [IDA])): 1.5076229999999999},
 'dividend yield': {(Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(1251 [CAJ])): 7.4899999999999993,
  (Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(1315 [CBT])): 5.3900000000000006,
  (Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(3212 [GILD])): 3.29,
  (Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(3460 [HAS])): 4.0599999999999996,
  (Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(3798 [IDA])): 3.04},
 'interest coverage': {(Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(1251 [CAJ])): 227.99559500000001,
  (Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(1315 [CBT])): 4.5714290000000002,
  (Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(3212 [GILD])): 8.8230450000000005,
  (Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(3460 [HAS])): 9.5895290000000006,
  (Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(3798 [IDA])): 3.1692089999999999},
 'marketcap': {(Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(1251 [CAJ])): 21652842873.0,
  (Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(1315 [CBT])): 1471969134.0,
  (Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(3212 [GILD])): 98467576445.0,
  (Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(3460 [HAS])): 9171245233.0,
  (Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(3798 [IDA])): 4308534238.0},
 'payout ratio': {(Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(1251 [CAJ])): 138.62,
  (Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(1315 [CBT])): 63.009999999999998,
  (Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(3212 [GILD])): 59.719999999999999,
  (Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(3460 [HAS])): 65.930000000000007,
  (Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(3798 [IDA])): 55.530000000000001},
 'pe_ratio': {(Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(1251 [CAJ])): 18.307486999999998,
  (Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(1315 [CBT])): 11.858447,
  (Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(3212 [GILD])): 18.533175,
  (Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(3460 [HAS])): 16.528395,
  (Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(3798 [IDA])): 18.540130000000001},
 'price': {(Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(1251 [CAJ])): 19.949999999999999,
  (Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(1315 [CBT])): 25.969999999999999,
  (Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(3212 [GILD])): 78.210000000000008,
  (Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(3460 [HAS])): 67.060000000000002,
  (Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(3798 [IDA])): 85.480000000000004},
 'sector': {(Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(1251 [CAJ])): True,
  (Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(1315 [CBT])): True,
  (Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(3212 [GILD])): True,
  (Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(3460 [HAS])): True,
  (Timestamp('2020-04-06 00:00:00+0000', tz='UTC', offset='C'),
   Equity(3798 [IDA])): True}}

Answer 1

Use a regular expression with str.extract

# I'm not sure of the column name, but it appears to be
# the second column if you reset the index
df.reset_index().iloc[:, 1].str.extract(r'\[([A-Z_]+)\]')